Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation

• URL: http://arxiv.org/abs/2307.13645v1
• Date: Tue, 25 Jul 2023 16:54:48 GMT
• Title: Learning Transferable Object-Centric Diffeomorphic Transformations for Data Augmentation in Medical Image Segmentation
• Authors: Nilesh Kumar, Prashnna K. Gyawali, Sandesh Ghimire, Linwei Wang
• Abstract summary: We propose a novel object-centric data augmentation model for medical image segmentation. It is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image. We demonstrate its effectiveness in improving kidney tumour segmentation when leveraging shape variations learned both from within the same dataset and transferred from external datasets.
• Score: 4.710950544945832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Obtaining labelled data in medical image segmentation is challenging due to the need for pixel-level annotations by experts. Recent works have shown that augmenting the object of interest with deformable transformations can help mitigate this challenge. However, these transformations have been learned globally for the image, limiting their transferability across datasets or applicability in problems where image alignment is difficult. While object-centric augmentations provide a great opportunity to overcome these issues, existing works are only focused on position and random transformations without considering shape variations of the objects. To this end, we propose a novel object-centric data augmentation model that is able to learn the shape variations for the objects of interest and augment the object in place without modifying the rest of the image. We demonstrated its effectiveness in improving kidney tumour segmentation when leveraging shape variations learned both from within the same dataset and transferred from external datasets.

Related papers

• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Advancing Medical Image Segmentation: Morphology-Driven Learning with Diffusion Transformer [4.672688418357066]
  We propose a novel Transformer Diffusion (DTS) model for robust segmentation in the presence of noise. Our model, which analyzes the morphological representation of images, shows better results than the previous models in various medical imaging modalities.
  arXiv  Detail & Related papers  (2024-08-01T07:35:54Z)
• Self-supervised Semantic Segmentation: Consistency over Transformation [3.485615723221064]
  We propose a novel self-supervised algorithm, textbfS$3$-Net, which integrates a robust framework based on the proposed Inception Large Kernel Attention (I-LKA) modules. We leverage deformable convolution as an integral component to effectively capture and delineate lesion deformations for superior object boundary definition. Our experimental results on skin lesion and lung organ segmentation tasks show the superior performance of our method compared to the SOTA approaches.
  arXiv  Detail & Related papers  (2023-08-31T21:28:46Z)
• Localizing Object-level Shape Variations with Text-to-Image Diffusion Models [60.422435066544814]
  We present a technique to generate a collection of images that depicts variations in the shape of a specific object. A particular challenge when generating object variations is accurately localizing the manipulation applied over the object's shape. To localize the image-space operation, we present two techniques that use the self-attention layers in conjunction with the cross-attention layers.
  arXiv  Detail & Related papers  (2023-03-20T17:45:08Z)
• Learning Explicit Object-Centric Representations with Vision Transformers [81.38804205212425]
  We build on the self-supervision task of masked autoencoding and explore its effectiveness for learning object-centric representations with transformers. We show that the model efficiently learns to decompose simple scenes as measured by segmentation metrics on several multi-object benchmarks.
  arXiv  Detail & Related papers  (2022-10-25T16:39:49Z)
• Robust Training Using Natural Transformation [19.455666609149567]
  We present NaTra, an adversarial training scheme to improve robustness of image classification algorithms. We target attributes of the input images that are independent of the class identification, and manipulate those attributes to mimic real-world natural transformations. We demonstrate the efficacy of our scheme by utilizing the disentangled latent representations derived from well-trained GANs.
  arXiv  Detail & Related papers  (2021-05-10T01:56:03Z)
• Neural Transformation Learning for Deep Anomaly Detection Beyond Images [24.451389236365152]
  We present a simple end-to-end procedure for anomaly detection with learnable transformations. The key idea is to embed the transformed data into a semantic space such that the transformed data still resemble their untransformed form. Our method learns domain-specific transformations and detects anomalies more accurately than previous work.
  arXiv  Detail & Related papers  (2021-03-30T15:38:18Z)
• Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation [53.49821324597837]
  Weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. We present a Context Decoupling Augmentation ( CDA) method to change the inherent context in which the objects appear. To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 dataset with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-03-02T15:05:09Z)
• Adversarial Semantic Data Augmentation for Human Pose Estimation [96.75411357541438]
  We propose Semantic Data Augmentation (SDA), a method that augments images by pasting segmented body parts with various semantic granularity. We also propose Adversarial Semantic Data Augmentation (ASDA), which exploits a generative network to dynamiclly predict tailored pasting configuration. State-of-the-art results are achieved on challenging benchmarks.
  arXiv  Detail & Related papers  (2020-08-03T07:56:04Z)
• Fine-grained Image-to-Image Transformation towards Visual Recognition [102.51124181873101]
  We aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image. We adopt a model based on generative adversarial networks to disentangle the identity related and unrelated factors of an image. Experiments on the CompCars and Multi-PIE datasets demonstrate that our model preserves the identity of the generated images much better than the state-of-the-art image-to-image transformation models.
  arXiv  Detail & Related papers  (2020-01-12T05:26:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.